145 lines
4.4 KiB
Python
145 lines
4.4 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from abc import ABC
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import os
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from typing import Any
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from typing import Optional
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from typing import TYPE_CHECKING
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from deprecated import deprecated
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from google.genai import types
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from .tool_context import ToolContext
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if TYPE_CHECKING:
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from ..models.llm_request import LlmRequest
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class BaseTool(ABC):
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"""The base class for all tools."""
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name: str
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"""The name of the tool."""
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description: str
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"""The description of the tool."""
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is_long_running: bool = False
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"""Whether the tool is a long running operation, which typically returns a
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resource id first and finishes the operation later."""
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def __init__(self, *, name, description, is_long_running: bool = False):
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self.name = name
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self.description = description
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self.is_long_running = is_long_running
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def _get_declaration(self) -> Optional[types.FunctionDeclaration]:
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"""Gets the OpenAPI specification of this tool in the form of a FunctionDeclaration.
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NOTE
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- Required if subclass uses the default implementation of
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`process_llm_request` to add function declaration to LLM request.
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- Otherwise, can be skipped, e.g. for a built-in GoogleSearch tool for
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Gemini.
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Returns:
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The FunctionDeclaration of this tool, or None if it doesn't need to be
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added to LlmRequest.config.
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"""
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return None
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async def run_async(
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self, *, args: dict[str, Any], tool_context: ToolContext
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) -> Any:
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"""Runs the tool with the given arguments and context.
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NOTE
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- Required if this tool needs to run at the client side.
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- Otherwise, can be skipped, e.g. for a built-in GoogleSearch tool for
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Gemini.
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Args:
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args: The LLM-filled arguments.
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tool_context: The context of the tool.
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Returns:
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The result of running the tool.
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"""
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raise NotImplementedError(f'{type(self)} is not implemented')
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async def process_llm_request(
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self, *, tool_context: ToolContext, llm_request: LlmRequest
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) -> None:
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"""Processes the outgoing LLM request for this tool.
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Use cases:
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- Most common use case is adding this tool to the LLM request.
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- Some tools may just preprocess the LLM request before it's sent out.
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Args:
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tool_context: The context of the tool.
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llm_request: The outgoing LLM request, mutable this method.
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"""
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if (function_declaration := self._get_declaration()) is None:
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return
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llm_request.tools_dict[self.name] = self
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if tool_with_function_declarations := _find_tool_with_function_declarations(
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llm_request
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):
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if tool_with_function_declarations.function_declarations is None:
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tool_with_function_declarations.function_declarations = []
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tool_with_function_declarations.function_declarations.append(
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function_declaration
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)
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else:
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llm_request.config = (
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types.GenerateContentConfig()
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if not llm_request.config
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else llm_request.config
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)
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llm_request.config.tools = (
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[] if not llm_request.config.tools else llm_request.config.tools
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)
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llm_request.config.tools.append(
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types.Tool(function_declarations=[function_declaration])
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)
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@property
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def _api_variant(self) -> str:
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use_vertexai = os.environ.get('GOOGLE_GENAI_USE_VERTEXAI', '0').lower() in [
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'true',
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'1',
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]
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return 'VERTEX_AI' if use_vertexai else 'GOOGLE_AI'
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def _find_tool_with_function_declarations(
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llm_request: LlmRequest,
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) -> Optional[types.Tool]:
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# TODO: add individual tool with declaration and merge in google_llm.py
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if not llm_request.config or not llm_request.config.tools:
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return None
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return next(
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(
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tool
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for tool in llm_request.config.tools
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if isinstance(tool, types.Tool) and tool.function_declarations
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),
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None,
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)
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